🔮 My Personal Open Source'rer Profile

This project focuses on using thermal imaging as a non-invasive method for detecting breast cancer. Repository for academic purposes.

Repository for method to analyse the relationship between germline variants and somatic mutations and alternative splicing in breast cancer patients based on RNA-Seq data,

📎 About MIMBCD-UI Project

Breast Cancer Pattern Recognition through Association Rule Mining

Using Machine Learning Methods to predicting Breast Cancer

The Algorithm that powers up the Intellibra Kit

Independent evaluation of a multi-view multi-task convolutional neural network breast cancer classification model using Finnish mammography screening data

This repository contains implementations and models related to the semantic segmentation of breast images. The main goal is to apply fine-tuning to the Semantic-SAM model to enhance accuracy in the segmentation of mammary structures(breast).

Medical Image processing project.

This project provides the classification of DNA sequences for Breast cancer prediction which into promoter regions associated. Using machine learning and deep learning techniques, I analyze and try to predict sequence data for negative and positive answers in cancer prediction.

Breast Cancer H&E classification of Images and Image Generation

The Breast Radar-based Image Quality Analysis (BRIQS) Framework was created as part of a Master's Year (MAI Biomedical Engineering) Project at Trinity College Dublin. BRIQS is a free and open-source framework for Microwave Radar-based Imaging. It builds upon the BRIGID phantom dataset and MERIT software.

NTU Deep Learning Medical Image course

Segmentation of Breast Cancers using various segmentation loss functions

This project develops a machine learning-based onsite health diagnostic system, facilitating real-time analysis and early detection of health conditions. By integrating data from various sources, it offers personalized insights and enhances healthcare accessibility.

Histomic Prognostic Signature (HiPS): A population-level computational histologic signature for invasive breast cancer prognosis

A web app to demonstrate the usage of Wasm-iCARE to calculate the absolute risk of breast cancer.

Predicting breast cancer survival using machine learning models

Breast Cancer Prediction with Hybrid Filter-Wrapper Feature Selection